WO1997038210A1

WO1997038210A1 - Process and plant for operating a block-type thermal power station

Info

Publication number: WO1997038210A1
Application number: PCT/DE1997/000659
Authority: WO
Inventors: Manfred Herbst
Original assignee: Siemens Aktiengesellschaft
Priority date: 1996-04-03
Filing date: 1997-04-01
Publication date: 1997-10-16
Also published as: KR20000005170A; CN1218536A; EP0891472A1; CA2250905A1; HRP970183A2

Abstract

Block-type thermal power stations generate demand-dependent useful energy, in particular current as electric energy and/or heat or cold as thermal energy. A prospective calculation of the current and heat demand for at least the next 24h is calculated based on different presently available criteria, and the consumption integrals that may be derived therefrom are determined and evaluated. The operation of the plant, preferably in the high tariff phase, is derived therefrom with optimum evaluation of the incoming and outgoing energies. The corresponding plant contains a processor unit in an automating machine, which may in particular be a processor unit already available in the plant for which an appropriate software is created. An energy optimisation program for block-type thermal power stations is thus set up with the available data.

Description

description

Process for operating a combined heat and power plant and system for carrying out the process

The invention relates to a method for operating a combined heat and power plant (CHP) with which demand-dependent useful energy, such as in particular electricity as electrical energy on the one hand and / or heating or cooling as thermal energy on the other hand, is generated, the electricity and heat requirement at least on a daily basis is forecast over the next 24 hours. In addition, the invention also relates to the associated system for carrying out a method for operating a cogeneration unit (CHP) with control units for operating the cogeneration unit and an associated optimization computer including processor unit and memory.

Combined heat and power plants (CHP) are understood to mean power generation plants with simultaneous use of heat, the primary energy being, for example, various gases and light or heavy heating oil by means of internal combustion engines, i.e. Piston engines or gas turbines, is implemented. Boilers are also used to cover heat peaks.

The particular benefit of CHP plants is the high overall efficiency of around 85% to 92% with which the primary energy used is converted into electricity and useful heat. In particular, the approximately 40% share of high-quality electrical energy and the simultaneous use of electricity and heat characterize the yield of combined heat and power plants.

From the publication BWK Fuel-Heat-Power 41 (1989), No. 6, pages 273 to 277, a daily use optimization for power plant systems with combined heat and power has been proposed. In the context of this study, the applicability and the benefit of powerful software for day-to-day optimization is proven. The system is However, especially intended for the realization at public utilities, which operate, among other things, a thermal power station. The special conditions of CHPs have not been examined in detail.

The problems of cogeneration plants are described in the publications BWK Fuel-Heat-Power 47 (1995), No. 11/12, pages 461 to 464 and BWK 48 (1996), No. 1/2, pages 61 to 66. The importance of dynamics in the planning and operation of

Combined heat and power plants examined. It is assumed that so far there is no verified instrument for the mathematical determination of daily courses of heat supply for combined heat and power plants. In particular, the question of whether typed reference profiles of different objects lead to acceptable results cannot be answered due to the lack of investigations. On this basis, it is required to determine dynamic computing aids for dynamic planning. In practice, comparative variables are determined on the basis of operational tests, which should lead to operational optimization. In fact, manual intervention is still largely necessary.

Furthermore, BWK Fuel-Heat-Power 47 (1995), No. 11/12, pages 476 to 479 describe that an operational optimization of combined heat and power plants can take place by integrating a PC-based energy management system into the control technology. It is essential that in addition to the data to be transferred, the data must also be available on the PC, which reflect the economic boundary conditions.

In contrast, the object of the invention is to propose a method and the associated system for operating combined heat and power plants, with which an optimization of operation can be carried out largely automatically. The object is achieved according to the invention in that the prognosis is a forward-looking calculation based on currently available, different criteria and in that an automated evaluation of the consumption integrals derived therefrom is determined, the selection and / or regulation of the generation of electricity and heat based on the energy ¬ cost is optimized in the different tariff phases. Different designed, self-correcting daily type curves are used as criteria for the predictive calculation. A number of parameters can be adjusted during operation.

The invention pursues a self-contained concept specifically for the operation of cogeneration plants. A decisive improvement in profitability is therefore possible through the following changes in the operating behavior of the system:

- Concentration of the plant operation on the high electricity tariff phases - Best possible use of the generated thermal energy

- greatest possible annual service life of the power generating sets

- Optimization of the incoming and outgoing energies.

In the invention, therefore, an optimized selection of the electricity and heat generation takes place solely on the basis of the energy costs. In this case, for the predictive calculation of electricity and heat for the following 24 hours, consumption curves are advantageously determined from averaged 15-minute values, which are synchronized with a maximum detection by the utility company.

In the associated system for carrying out the method according to the invention, the memory is suitable for storing day type curves and input units are available for entering variables, so that means for selecting the operating data for energy optimization are implemented. In particular, the system already exists for this Processor unit used. The means for selecting the operating data are the software for the processor.

The invention thus creates a new control and optimization program or system for combined heat and power plants. The corresponding software is preferably stored in the assembler code by means of neural networks in an automation device.

Further details and advantages of the invention result from the following description of the figures of exemplary embodiments with reference to the drawing in conjunction with the further claims. Show it

1 shows the heat demand for summer, transition time and winter that can be called up by a combined heat and power plant, FIG. 2 heat demand and unit running time in the transition time marked in FIG. 1, work being carried out either with or without a cooling tower,

3 shows the automatic curve determination for the example of

Transition time in FIG 1 and 4 a structure plan for an energy optimization program.

Combined heat and power plants (BHKW) are used to generate electricity and heat using primary energy. CHP units are small power plants that generate energy where it is needed. Primary energy (e.g. natural gas, diesel) or waste energies (e.g. sewage gas, landfill gas, mine gas) are efficiently and environmentally friendly converted into useful energy (heat, cold, electricity).

In addition to boilers for heat peaks and peripheral systems for integrating electricity and heat, the focus is on a gas engine unit with a synchronous generator. The exhaust gas, cooling water and other heat sources of the engine are used as heating or process energy. CHP plants are built with electrical outputs from 100 kW to around 20,000 kW. A CHP plant is made up of one or more - a maximum of six to eight - units and any necessary top boilers.

As is known, the efficiency of the CHPs results from the high overall efficiency through electricity and heat generation. Depending on the operating conditions, efficiencies of 85% to 90% are achieved. The opportunities for improvement for the

The design and constellation of the CHP components are largely exhausted.

1 to 3 each show daily profiles of the energy requirement. The time is plotted on the hourly grid as the abscissa and the power in 1000 kW each as the ordinate. 1 shows three curves 1 to 3, which represent the heat requirement during a day in summer, in the transitional period and in winter. You can see a significant curve shape with peaks, especially in the morning, at noon and in the evening. Such curves are known in principle.

FIG. 2 shows curve 2 from FIG. 1. The respective heat requirement is realized through an aggregate running time. In particular, two or three units are operated, each of which generates 1.7 MW, for example. In the present example, a demand of more than 5 MW can thus be taken into account, as is required, for example, in the early morning hours. Since the demand decreases in the midday hours, but increases in the evening hours to, for example, 7 MW, the three units can activate a storage unit in the midday time, from which additional heat is drawn in the evening hours.

According to curve 2 in FIG. 1 and FIG. 2, the heat requirement can have a specific bandwidth of, for example, 500 kW. As detailed below is shown, the exact curves can be determined in detail by external correction factors, which is shown for example in FIG. 3. This results in an energy optimization program for the daily routine.

The energy optimization program essentially comprises the following functions:

1) Consumption forecast: A reliable and highly precise consumption forecast for electricity and heat is continuously produced for the following 24 hours. The consumption curves consist of the averaged 15 minute values, which are synchronized with the maximum recording of the EVU.

The forecast is calculated, for example, on the basis of eight different self-correcting daily type curves. The outside temperature, the vectors of the last four measured values for electricity and heat consumption and a programmed calendar for the day types are included as parameters in the calculation of the forecast.

The following parameters are provided: a) Storage of daily type curves for electricity and heat - normal working day

- Transitional working day (earlier end of business)

- day before working day, e.g. Sunday

- day after working day, e.g. Saturday

- day between two working days - two-shift operation working day

- Special day 1 (bridge day)

- Special day 2 (interim operation)

All day types are continuously and automatically corrected with the average actual value of the last five days of the same type. b) Correction functions for daily curves

- outside temperature

A time-dependent comparison value has a corrective effect on the heat demand curve. - Hourly heat output

The steepness of thermal changes in performance is compared with the output curve and corrects the heat demand curve.

- Average hourly power consumption The steepness of electrical power changes is compared with the output curve and corrects the current demand curve.

- humidity

If necessary, the air humidity is measured and the heat demand curve is adjusted based on the comparative value.

- brightness (sun / cloudy)

If necessary, the daylight intensity is measured and the current and heat demand curve is adapted based on the comparative value. c) Calendar function

- Term 1 year (12 months)

- Entry of day types

2) Assessment of energy costs The types of energy and the periods in which these energies are required are arranged in a list of values with up to 20 levels. In particular, this means the most expensive form of energy, e.g. Peak current, has the value "1" and the cheapest form of energy, e.g. Heating at night, has the value "20"

The system determines the switching of the valence levels based on the stored high and low tariff periods and the calculated consumption curves. Example of the evaluation scale with energy cost points:

Pt.

1. Peak power generation 40

2. HT power generation 22 3. HT heat generation 8

4. HT heat storage for NT time 7

5. NT heat storage drain 6

6. NT heat generation 5

7.Peak boiler calorific value 2 8.Empty gas storage (primary energy) 2

9.Fill gas storage (primary energy) 0

10.Peak kettle 0

11. Cooling tower for heat dissipation -6

12.Recooling with fresh water -4 13.Gas (primary energy) -4

14. Waste gas (primary energy) -4

15.Diesel (primary energy) 0

16. Load shedding rank 1 ... 16 -10

17. Peak gas (primary energy) -40

3) Curve adjustment

The correction factors actually measured are compared with the factors of the curve stored in the program and the curve is then updated by a corresponding parallel shift up or down. The amplification of the correction factors is specified individually. The runtime optimization always works with the current curves.

4) Energy data The number and functions of the specific system components are entered in the user interface of the computer program. give. The availability of the individual systems is automatically corrected by the external CHP / MSR technology. The ideal operating modes are regulated on the basis of generation and consumption data as well as part-load behavior of the individual components.

5) Runtime optimization

The energy optimization program uses the forecast heat demand over the next 24 to 48 hours to determine the integral of the heat demand during the low-energy tariff phases and compares the future demand with the available and required heat storage capacity.

As far as possible, the electricity / heat generation units are then operated in such a way that the entire heat requirement is generated by units during the high electricity tariff phase.

If the performance of the aggregates or the capacity of the heat storage does not make this possible, in accordance with the lack of thermal energy and energy quality-dependent aggregates or boilers for a base load generation, operation is also continued in the low tariff phase, so that the storage content is then on the lower one Is sufficient.

In the same way and with similar integral formations and calculation of the respective energy costs, the sensible use of all generation and re-cooling components is regulated. The energy optimization program thus usually allows a reduction in the installed capacity, i.e. the ideal use of the units and boilers. Reduction of the total investment.

In addition, operating at the optimum cost saves primary energy and generates more high-quality energy, thus improving the profit from operating the plant. Using the criteria described in detail above, software can be created with which an existing processor unit is operated. It is essential that all relevant data for the ongoing operation of the combined heat and power plant are recorded. The structure of such an energy optimization program can be seen in detail from the diagram according to FIG. 4, which is essentially self-explanatory.

In FIG. 4, specifically for a combined heat and power plant (CHP), 10 ff. Mean those units that are based on the heat requirement and 20 ff those units that are geared to the electricity requirement. 11 includes the formation and storage of day type curves for the heat requirement, for example nine day types, and 12 the correction determination for the heat requirement, from which a demand forecast for heat of, for example, 24 to 48 h is derived in unit 13. Correspondingly, unit 21 includes the formation and storage of daily type curves for the electricity demand, for example nine daily types, and 22 the correction determination for the electricity demand, from which the demand forecast for the electricity, for example for the next 24 to 48 hours, is given in unit 23 is leading. The latter is used in unit 24 to determine the heat that can be generated using cogeneration, and together with unit 13, a unit 25 for forecasting and utilizing the heat storage capacity is controlled with the demand forecast for the heat. In the same line, there is a unit 26 for electricity tariff intervals with grid-synchronized maximum detection and, if necessary, a requirement for peak power from the utility company.

A unit 30 follows for optimizing the runtime of the units in order to maximize the generation of electricity in high-tariff phases. The unit 30 is a unit 32 for

Selection of the required combined heat and power units and a unit 33 for fixing unnecessary, but available combined heat and power units. In the same line there is also a unit 34 in which a list of the evaluated energies, for example 20 values, is set up and a unit 35 for setting up the availability of delivery and acceptance, such as status, capacity etc., for example for 20 values. A downstream unit for energy value optimization is controlled by units 32 to 35, taking into account all energies to be discharged and their values. The unit 40 can be used to call up the individual requirements for the individual units 1 to n, such as boilers, storage units, cooling towers, etc.

The units shown in the structure plan according to FIG. 4 are simulated by suitable software, which is implemented in a simple manner in the assembler code.

If no exact analytical relationships can be determined for the energy optimization program described, neural networks can be advantageously used based on approximations, which are trained in such a way that they adjust themselves to the respective circumstances in a self-learning manner.

Claims

claims

1. Method for operating a combined heat and power plant (CHP) with which, depending on demand, useful energy, such as, in particular, electricity as electrical energy on the one hand and / or heat or cold as thermal energy on the other hand, is generated, the electricity and heat requirement being at least daily over the next 24th h is predicted, d ad urchgekenn ¬ indicates that the forecast is a predictive calculation based on currently available, different

Criteria is and that the consumption integrals which can be derived therefrom are determined with an automatic evaluation, the selection and / or regulation of the electricity and heat generation being optimized on the basis of the energy costs in the different tariff phases.

2. The method of claim 1, d a d u r c h g e k e n n ¬ z e i c h n e t that the electricity is largely generated in the Hoch¬ tariff phase.

3. The method of claim 1, d a d u r c h g e k e n n ¬ z e i c h n e t that for the predictive calculation of electricity and heat for the following 24 hours consumption curves are determined from averaged 15-minute values, which are synchronized with a maximum recording of the power supply company.

4. The method of claim 1 and 3, so that differently designed, self-correcting day-type curves are used as criteria for the predictive calculation.

5. The method of claim 4, d a d u r c h g e k e n n ¬ z e i c h n e t that as a day type curves are used as a basis: - normal working day

- Transitional working day (end of spring)

- Day before working day, e.g. Sunday - Day after working day, e.g. Saturday

- day between two working days

- Two-shift operation but working day

- Special day 1 (bridge day) - Special day 2 (interim operation).

6. The method according to any one of the preceding claims, d a d u r c h g e k e n n z e i c h n e t that parameters of the existing criteria are the outside temperature, the vectors of at least the last four measured values for electricity and heat consumption and a programmed calendar for the day types.

7. The method of claim 5, d a d u r c h g e k e n n - z e i c h n e t that correction functions for the daily curves are defined.

8. The method according to claim 7, d a d u r c h g e k e n n ¬ z e i c h n e t that changes or steepness in the course of outside temperature, heat output in the hourly average, current demand in the hourly average, the humidity and / or daylight are included in the correction functions.

9. The method according to claim 8, d a d u r c h g e k e n n - z e i c h n e t that the changes in heat output and / or power consumption are recorded on an hourly average.

10. The method according to any one of the preceding claims, d a d u r c h g e k e n n z e i c h n e t that the energy costs for the individual types of energy and different periods are taken into account.

11. The method according to claim 10, dadurchge ¬ indicates that the types of energy and the periods in which the individual types of energy are required are provided with values, for example in 20 steps.

12. The method as claimed in claim 11, so that the system uses the high-tariff and low-tariff periods stored and the calculated consumption curves to switch the valence levels from the system.

13. System for carrying out the operating method according to claim 1 or one of claims 2 to 12 with control units for operating the combined heat and power plant and an associated optimization computer including processor unit and memory, characterized in that the memory is suitable for storing day type curves, and that Input units are designed for entering variables, so that means are available for selecting the operating data for energy optimization.

14. System according to claim 13, so that an existing processor unit is used.

15. System according to claim 12, so that the means for selecting the operating data are implemented as software.

16. System according to claim 13, so that the software represents a control and optimization program or system (LEOPS) for combined heat and power plants (CHP).